We trained several image-based classifiers on the full RVL-CDIP training set, including convolutional neural network (CNN) architectures and image transformer architectures.
We consider the following CNNs:
VGG-16, ResNet-50, GoogLeNet, and AlexNet (each uses the architecture from [1]}, [2]}, [3]}, [4]}, respectively).
All of these CNN models were originally trained and evaluated on the RVL-CDIP corpus by [5]}.
Our analysis uses PyTorch [6]} implementations of these models.
In addition, we evaluate the LayoutLMv2-base model architecture [7]}, which uses a language model trained on the locations and sizes of words in documents.
As the original RVL-CDIP corpus does not provide textual information, we use the Tesseract https://github.com/tesseract-ocr/tesseract OCR engine to extract words and their locations in each document image.
Our analysis uses the Hugging Face [8]} implementation of LayoutLMv2.
